Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding to better use unlabeled data, particularly from underrepresented classes. A re-sampling strategy is also introduced to mitigate bias towards well-represented classes, ensuring equitable class representation. Through extensive experiments in 3D SSL, our method surpasses state-of-the-art counterparts in classification and detection tasks, highlighting its effectiveness in tackling data imbalance. This approach presents a significant advancement in SSL for 3D datasets, providing a robust solution for data imbalance issues.

翻译：半监督学习（SSL）利用有限的标注数据和丰富的未标注数据，但在数据不平衡场景下面临挑战，尤其是在三维领域。本研究探讨了将类别级置信度作为三维SSL学习状态的指示器，提出了一种新颖方法，利用动态阈值以更好地利用未标注数据，特别是来自代表性不足类别的数据。同时引入重采样策略以减轻对代表性良好类别的偏倚，确保各类别的公平表征。通过在三维SSL中的大量实验，我们的方法在分类与检测任务上超越了现有最优方法，突显了其在应对数据不平衡问题上的有效性。该方法为三维数据集的半监督学习提供了重要进展，为数据不平衡问题提供了稳健的解决方案。